TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa
Pedro Alonso, Rajkumar Saini, György Kovács · 2020
Hate speech detection on social media platforms is crucial as it helps to avoid severe harm to marginalized people and groups.The application of Natural Language Processing (NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection.The expression of hate, however, is varied and ever-evolving.Thus better detection systems need to adapt to this variance.Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions.In this paper, we discuss our entry to one such competition, namely the English version of sub-task A for the OffensEval competition.Our contribution can be perceived through our results, that was first an F1-score of 0.9087, and with further refinements described here climb up to 0.9166.It serves to give more support to our hypothesis that one of the variants of BERT, namely RoBERTa can successfully differentiate between offensive and non-offensive tweets, given the proper preprocessing steps.